Xu Yue, Pan Quan, Wang Zengfu, Hu Baoquan
School of Automation, Northwestern Polytechnical University, Xi'an 710129, China.
School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, China.
Entropy (Basel). 2024 Sep 26;26(10):823. doi: 10.3390/e26100823.
To address the complex maneuvering characteristics of hypersonic targets in adjacent space, this paper proposes an LSTM trajectory estimation method combined with the attention mechanism and optimizes the model from the information-theoretic perspective. The method captures the target dynamics by using the temporal processing capability of LSTM, and at the same time improves the efficiency of information utilization through the attention mechanism to achieve accurate prediction. First, a target dynamics model is constructed to clarify the motion behavior parameters. Subsequently, an LSTM model incorporating the attention mechanism is designed, which enables the model to automatically focus on key information fragments in the historical trajectory. In model training, information redundancy is reduced, and information validity is improved through feature selection and data preprocessing. Eventually, the model achieves accurate prediction of hypersonic target trajectories with limited computational resources. The experimental results show that the method performs well in complex dynamic environments with improved prediction accuracy and robustness, reflecting the potential of information theory principles in optimizing the trajectory prediction model.
为解决临近空间高超声速目标复杂的机动特性问题，本文提出一种结合注意力机制的长短期记忆网络（LSTM）轨迹估计方法，并从信息论角度对模型进行优化。该方法利用LSTM的时间处理能力捕捉目标动态，同时通过注意力机制提高信息利用效率以实现精确预测。首先，构建目标动态模型以明确运动行为参数。随后，设计一种融入注意力机制的LSTM模型，使模型能够自动聚焦于历史轨迹中的关键信息片段。在模型训练中，通过特征选择和数据预处理减少信息冗余，提高信息有效性。最终，该模型在有限计算资源下实现对高超声速目标轨迹的精确预测。实验结果表明，该方法在复杂动态环境中表现良好，预测精度和鲁棒性得到提高，体现了信息论原理在优化轨迹预测模型方面的潜力。
